Fundamentally, methods for the suppression of noise in volume data are known. For example, linear low-pass filtering can be used to effectively reduce the noise, but in this case the clarity of the data material and thus the quality of the display of small structures deteriorate. This simple approach can thus be used only to a limited extent in order to improve the image material.
Another method is based on two-dimensional or three-dimensional iterative filtering of the data material, with information about the position and orientation of edges being included in each step. Reference is made in this context, for example, to T. Chan, S. Osher, and J. Shen; The digital TV filter and non-linear denoising; http://citeseer.nj.nec.com/article/chan01digital.html, 1999. Tech. Report CAM 99-34, Department of Mathematics, UCLA Los Angeles, Calif., 1999; IEEE Trans. Image Process., to appear [date called up May 15, 2003] and Aurich V., et al.; Non-linear Gaussian Filters Performing Edge Preserving Diffusion; Proceedings 17th DAGM Symposium on pattern recognition, Springer 538-545, 1995.
On the basis of the “central limit-value record”, these methods cited above lead to a Gaussian filter characteristic which frequently does not correspond to the normal image impression for radiologists, and is thus rejected. A further problem is the delay time of algorithms such as these, which is in the region of minutes per axial slice because of the large number of iterations, thus making the method unsuitable for clinical use.
The German patent application, which was not published prior to this application, with the file reference DE 10 2004 008 979.5-53 proposed an improvement in the method for filtering of tomographic 3D displays of an examination object in which a volume model is used to display the examination object, subdivides the volume of the examination object into a large number of three-dimensional image voxels with individual image values, and the image value of each voxel reproduces one object-specific characteristic of the examination object in this volume. Furthermore, on the basis of the reconstruction of the entire volume of each image voxel, the variances are calculated in a predetermined area or radius R, in order to determine sudden contrast changes and their spatial orientation with their tangential planes T, with the image values within the tangential plane T being filtered using a two-dimension convolution process, and with the original voxel data items then being mixed with the filtered voxel data items in a weighted manner. The entire disclosure content and the of Patent Application DE 10 2004 008 979.5-53 is incorporate herein by reference, in its entirety and for all purposes.
In principle, this method is admittedly a step in the right direction, but it may have a disadvantage in that this method uses non-defined two-dimensional filters non-iteratively, and these must be calculated explicitly for each voxel. In consequence, the method is highly computation-intensive and may not offer an optimum solution for practical use.
In addition to the unreasonably high level of computation complexity, known CT displays are also subject to the further problem of the so-called blooming effect, as a result of which plaques with a high CT value, for example calcification areas, apparently have a larger volume, and remaining vessel diameters are measured incorrectly, specifically as being too small.